Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. EEG signals are received from a sensor coupled to a user. Contextual information from one or both of the user and the user's environment is also received. The EEG signals are processed in real time using a machine learning model to predict an action of the user, which is associated with the contextual information. Output associated with the predicted action is then generated.

Abstract: An electroencephalogram (EEG) sensor is disclosed. The EEG sensor includes a housing defining a chamber capable of storing a gel, the housing includes a first and a second chamber wall, the walls each comprising a corresponding access port located on a common axis extending through the housing; an electrically-conductive probe with a probe tip extending at least partially through the chamber along the axis, at least a portion of the probe tip being exposed to the chamber; an electrical terminal located at an outer surface of the second chamber wall, the electrical terminal being in electrical communication with the probe tip through the access port at the second chamber wall; and a compliant member mechanically coupled to the access port at the first chamber wall capable of compressing, thereby providing a dispense pathway from the chamber through the access port at the first chamber wall.

Abstract: An electroencephalogram (EEG) system is disclosed. The EEG system includes an EEG controller and an EEG sensor that includes a contact surface, the contact surface including an electrically-conductive portion in communication with the EEG controller and a sensor release element in communication with the EEG controller, the sensor release element being configured to perform, in response to a signal from the EEG controller, a release action to reduce adhesion of an electrically-conductive gel between the contact of the sensor and the user's skin.

Abstract: A method for obtaining an electroencephalogram (EEG) of a user is disclosed. A reference sensor is attached to the user by connecting a first component of the reference sensor to a second component of the reference sensor, at least a portion of the first component being sub-dermally implanted on or adjacent to a mastoid process of the user. At least one active sensor is attached to the user. A first signal is detected from the reference sensor simultaneously as a second signal is detected from the at least one active sensor. The EEG is obtained based on the first signal and the second signal.

Abstract: Methods, systems, and apparatus for generating images that blend an appearance of a display with an environment of the display. In some aspects, output is provided from a display that occludes an object. A gaze direction is determined for an observer located within an environment of the display. An image is generated based on the determined gaze direction of the observer. The generated image is configured to blend an appearance of the display with the environment of the display. The generated image is displayed on the display directed to the observer.

Abstract: Methods, systems, and apparatus for providing indications of occluded objects. In some aspects a method includes the actions of determining a position of an observer whose view of an object is obstructed by a barrier; determining a position of the object relative to the observer; generating an indication of the position of the object based at least in part on the determined position of the observer; and displaying the indication of the position of the object on a display located between the observer and the barrier.

Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. EEG signals are received from a sensor coupled to a user. Contextual information from one or both of the user and the user's environment is also received. The EEG signals are processed in real time using a machine learning model to predict an action of the user, which is associated with the contextual information. Output associated with the predicted action is then generated.

Abstract: An example method may include receiving, from a client computing device, an indication of a target drop-off spot for an object within a first virtual model of a first region of a delivery destination. A second virtual model of a second region of the delivery destination may be determined based on sensor data received from one or more sensors on a delivery vehicle. A mapping may be determined between physical features represented in the first virtual model and physical features represented in the second virtual model to determine an overlapping region between the first and second virtual models. A position of the target drop-off spot within the second virtual model may be determined based on the overlapping region. Based on the position of the target drop-off spot within the second virtual model, the delivery vehicle may be navigated to the target drop-off spot to drop off the object.

Abstract: A sensor device includes a sensor housing defining a channel extending along a channel axis through the housing from a first side of the sensor housing to a second side of the sensor housing opposite the first side, at least one contact electrode extending from the first side of the housing, an electrically-conducting lead attached to the housing in electrical communication with the at least one contact electrode, and a locking mechanism located in the channel permitting one-way axial motion of a thread threaded through the channel from the first side to the second side.

Abstract: A method for obtaining an electroencephalogram (EEG) of a user is disclosed. A reference sensor is attached to the user by connecting a first component of the reference sensor to a second component of the reference sensor, at least a portion of the first component being sub-dermally implanted on or adjacent to a mastoid process of the user. At least one active sensor is attached to the user. A first signal is detected from the reference sensor simultaneously as a second signal is detected from the at least one active sensor. The EEG is obtained based on the first signal and the second signal.

Abstract: A method for generating an EEG signal is disclosed. Multiple signals from multiple electrodes applied to a user's scalp are measured. The signals correspond to electrical activity generated by the user's brain. Each of the signals is evaluated using a machine learning algorithm in real-time to determine a quality of each of the signals. One or more switches is activated between the electrodes to electrically-connect a plurality of the electrodes. An EEG signal is measured using the parallel-connected electrodes.

Abstract: An electroencephalogram (EEG) system is disclosed. The EEG system includes an EEG controller and an EEG sensor that includes a contact surface, the contact surface including an electrically-conductive portion in communication with the EEG controller and a sensor release element in communication with the EEG controller, the sensor release element being configured to perform, in response to a signal from the EEG controller, a release action to reduce adhesion of an electrically-conductive gel between the contact of the sensor and the user's skin.

Abstract: A bioamplifier for analyzing electroencephalogram (EEG) signals is disclosed. The bioamplifier includes an input terminal for receiving an EEG signal from a plurality of sensors coupled to a user. The bioamplifier also includes an analogue-to-digital converter arranged to receive the EEG signal from the input terminal and convert the EEG signal to a digital EEG signal. A data processing apparatus within the bioamplifier is arranged to receive the digital EEG signal from the analogue-to-digital converter and programmed to process, in real time the digital EEG signal using a first machine learning model to generate a cleaned EEG signal having a higher signal-to-noise ratio than the digital EEG signal. The bioamplifier further includes a power source to provide electrical power to the analogue-to-digital converter and the data processing apparatus. The bioamplifier includes a housing that contains the analogue-to-digital converter, the data processing apparatus, the power source, and the sensor input.

Abstract: An electroencephalogram (EEG) sensor is disclosed. The EEG sensor includes a housing defining a chamber capable of storing a gel, the housing includes a first and a second chamber wall, the walls each comprising a corresponding access port located on a common axis extending through the housing; an electrically-conductive probe with a probe tip extending at least partially through the chamber along the axis, at least a portion of the probe tip being exposed to the chamber; an electrical terminal located at an outer surface of the second chamber wall, the electrical terminal being in electrical communication with the probe tip through the access port at the second chamber wall; and a compliant member mechanically coupled to the access port at the first chamber wall capable of compressing, thereby providing a dispense pathway from the chamber through the access port at the first chamber wall.

Abstract: A method for generating an EEG signal is disclosed. Multiple signals from multiple electrodes applied to a user's scalp are measured. The signals correspond to electrical activity generated by the user's brain. Each of the signals is evaluated using a machine learning algorithm in real-time to determine a quality of each of the signals. One or more of the signals is selected based on quality in real-time to provide one or more selected signals. An EEG signal is outputted corresponding to the electrical activity based on the selected signals.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving brain activity data of a user from a brain wave sensor. Identifying Alpha wave activity from the brain activity data. Determining a synchronization timing for presenting content to the user such that the content appears on a display device during a predetermined phase of the Alpha wave activity based on the Alpha wave activity. Causing the content to be displayed on the display device according to the synchronization timing, where the content includes a first content item and a second content item that is associated with the first content item.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving brain activity data of a user from a brainwave sensor and user physiological data from a non-brainwave sensor, where the brain activity data represents a brainwave pattern related to a physiological activity of the user and a brainwave pattern related to a mental activity of the user. Identifying a physiological action of the user based on the user physiological data. Identifying, within the brain activity data, a pattern that is representative of the identified physiological action. Filtering the brain activity data to lessen a contribution of the pattern representative of the identified physiological action to the brain activity data, thereby, providing filtered brain activity data.

Abstract: Methods, systems, and apparatus for providing indications of occluded objects. In some aspects a method includes the actions of determining a position of an observer whose view of an object is obstructed by a barrier; determining a position of the object relative to the observer; generating an indication of the position of the object based at least in part on the determined position of the observer; and displaying the indication of the position of the object on a display located between the observer and the barrier.

Abstract: Techniques and mechanisms to provide for improved image display in an area of overlapping projections. In an embodiment, a multi-layer projection screen comprises light sources and collimation structures each disposed over a corresponding one of such light sources. A first collimation structure disposed over a first light source collimates first light from the first light source. The first collimation structure further receives and redirects second light from a second light source disposed under a second collimation structure that adjoins the first collimation structure. In another embodiment, the first collimation structure redirects the other light from the second light source away from the direction of collimation of the first light. A stray light rejection layer of the multi-layer projection screen passes a majority of the first light for inclusion as part of a projected image, and prevents a majority of the second light from inclusion in the projected image.

Abstract: Techniques and mechanisms to provide for improved image display in an area of overlapping projections. In an embodiment, a multi-layer projection screen comprises light sources and collimation structures each disposed over a corresponding one of such light sources. A first collimation structure disposed over a first light source collimates first light from the first light source. The first collimation structure further receives and redirects second light from a second light source disposed under a second collimation structure that adjoins the first collimation structure. In another embodiment, the first collimation structure redirects the other light from the second light source away from the direction of collimation of the first light.